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1302.6804 | Florence Dupin de Saint-Cyr | Florence Dupin de Saint-Cyr, Jerome Lang, Thomas Schiex | Penalty logic and its Link with Dempster-Shafer Theory | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-204-211 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Penalty logic, introduced by Pinkas, associates to each formula of a
knowledge base the price to pay if this formula is violated. Penalties may be
used as a criterion for selecting preferred consistent subsets in an
inconsistent knowledge base, thus inducing a non-monotonic inference relation.
A precise formalization and the main properties of penalty logic and of its
associated non-monotonic inference relation are given in the first part. We
also show that penalty logic and Dempster-Shafer theory are related, especially
in the infinitesimal case.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:44 GMT"
}
] | 1,362,009,600,000 | [
[
"de Saint-Cyr",
"Florence Dupin",
""
],
[
"Lang",
"Jerome",
""
],
[
"Schiex",
"Thomas",
""
]
] |
1302.6805 | Kazuo J. Ezawa | Kazuo J. Ezawa | Value of Evidence on Influence Diagrams | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-212-220 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce evidence propagation operations on influence
diagrams and a concept of value of evidence, which measures the value of
experimentation. Evidence propagation operations are critical for the
computation of the value of evidence, general update and inference operations
in normative expert systems which are based on the influence diagram
(generalized Bayesian network) paradigm. The value of evidence allows us to
compute directly an outcome sensitivity, a value of perfect information and a
value of control which are used in decision analysis (the science of decision
making under uncertainty). More specifically, the outcome sensitivity is the
maximum difference among the values of evidence, the value of perfect
information is the expected value of the values of evidence, and the value of
control is the optimal value of the values of evidence. We also discuss an
implementation and a relative computational efficiency issues related to the
value of evidence and the value of perfect information.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:51 GMT"
}
] | 1,362,009,600,000 | [
[
"Ezawa",
"Kazuo J.",
""
]
] |
1302.6806 | Pascale Fonck | Pascale Fonck | Conditional Independence in Possibility Theory | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-221-226 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic conditional independence is investigated: we propose a
definition of this notion similar to the one used in probability theory. The
links between independence and non-interactivity are investigated, and
properties of these relations are given. The influence of the conjunction used
to define a conditional measure of possibility is also highlighted: we examine
three types of conjunctions: Lukasiewicz - like T-norms, product-like T-norms
and the minimum operator.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:56 GMT"
}
] | 1,362,009,600,000 | [
[
"Fonck",
"Pascale",
""
]
] |
1302.6807 | Robert Fung | Robert Fung, Brendan del Favero | Backward Simulation in Bayesian Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-227-234 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Backward simulation is an approximate inference technique for Bayesian belief
networks. It differs from existing simulation methods in that it starts
simulation from the known evidence and works backward (i.e., contrary to the
direction of the arcs). The technique's focus on the evidence leads to improved
convergence in situations where the posterior beliefs are dominated by the
evidence rather than by the prior probabilities. Since this class of situations
is large, the technique may make practical the application of approximate
inference in Bayesian belief networks to many real-world problems.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:02 GMT"
}
] | 1,362,009,600,000 | [
[
"Fung",
"Robert",
""
],
[
"del Favero",
"Brendan",
""
]
] |
1302.6809 | Dan Geiger | Dan Geiger, Azaria Paz, Judea Pearl | On Testing Whether an Embedded Bayesian Network Represents a Probability
Model | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-244-252 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Testing the validity of probabilistic models containing unmeasured (hidden)
variables is shown to be a hard task. We show that the task of testing whether
models are structurally incompatible with the data at hand, requires an
exponential number of independence evaluations, each of the form: "X is
conditionally independent of Y, given Z." In contrast, a linear number of such
evaluations is required to test a standard Bayesian network (one per vertex).
On the positive side, we show that if a network with hidden variables G has a
tree skeleton, checking whether G represents a given probability model P
requires the polynomial number of such independence evaluations. Moreover, we
provide an algorithm that efficiently constructs a tree-structured Bayesian
network (with hidden variables) that represents P if such a network exists, and
further recognizes when such a network does not exist.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:13 GMT"
}
] | 1,362,009,600,000 | [
[
"Geiger",
"Dan",
""
],
[
"Paz",
"Azaria",
""
],
[
"Pearl",
"Judea",
""
]
] |
1302.6810 | Robert P. Goldman | Robert P. Goldman, Mark S. Boddy | Epsilon-Safe Planning | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-253-261 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce an approach to high-level conditional planning we call
epsilon-safe planning. This probabilistic approach commits us to planning to
meet some specified goal with a probability of success of at least 1-epsilon
for some user-supplied epsilon. We describe several algorithms for epsilon-safe
planning based on conditional planners. The two conditional planners we discuss
are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear
conditional planner, PLINTH. We present a straightforward extension to
conditional planners for which computing the necessary probabilities is simple,
employing a commonly-made but perhaps overly-strong independence assumption. We
also discuss a second approach to epsilon-safe planning which relaxes this
independence assumption, involving the incremental construction of a
probability dependence model in conjunction with the construction of the plan
graph.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:19 GMT"
}
] | 1,362,009,600,000 | [
[
"Goldman",
"Robert P.",
""
],
[
"Boddy",
"Mark S.",
""
]
] |
1302.6811 | Peter Haddawy | Peter Haddawy | Generating Bayesian Networks from Probability Logic Knowledge Bases | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-262-269 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for dynamically generating Bayesian networks from
knowledge bases consisting of first-order probability logic sentences. We
present a subset of probability logic sufficient for representing the class of
Bayesian networks with discrete-valued nodes. We impose constraints on the form
of the sentences that guarantee that the knowledge base contains all the
probabilistic information necessary to generate a network. We define the
concept of d-separation for knowledge bases and prove that a knowledge base
with independence conditions defined by d-separation is a complete
specification of a probability distribution. We present a network generation
algorithm that, given an inference problem in the form of a query Q and a set
of evidence E, generates a network to compute P(Q|E). We prove the algorithm to
be correct.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:25 GMT"
}
] | 1,362,009,600,000 | [
[
"Haddawy",
"Peter",
""
]
] |
1302.6812 | Peter Haddawy | Peter Haddawy, AnHai Doan | Abstracting Probabilistic Actions | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-270-277 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses the problem of abstracting conditional probabilistic
actions. We identify two distinct types of abstraction: intra-action
abstraction and inter-action abstraction. We define what it means for the
abstraction of an action to be correct and then derive two methods of
intra-action abstraction and two methods of inter-action abstraction which are
correct according to this criterion. We illustrate the developed techniques by
applying them to actions described with the temporal action representation used
in the DRIPS decision-theoretic planner and we describe how the planner uses
abstraction to reduce the complexity of planning.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:31 GMT"
}
] | 1,362,009,600,000 | [
[
"Haddawy",
"Peter",
""
],
[
"Doan",
"AnHai",
""
]
] |
1302.6814 | David Heckerman | David Heckerman, John S. Breese | A New Look at Causal Independence | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-286-292 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heckerman (1993) defined causal independence in terms of a set of temporal
conditional independence statements. These statements formalized certain types
of causal interaction where (1) the effect is independent of the order that
causes are introduced and (2) the impact of a single cause on the effect does
not depend on what other causes have previously been applied. In this paper, we
introduce an equivalent a temporal characterization of causal independence
based on a functional representation of the relationship between causes and the
effect. In this representation, the interaction between causes and effect can
be written as a nested decomposition of functions. Causal independence can be
exploited by representing this decomposition in the belief network, resulting
in representations that are more efficient for inference than general causal
models. We present empirical results showing the benefits of a
causal-independence representation for belief-network inference.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:44 GMT"
},
{
"version": "v2",
"created": "Sun, 17 May 2015 00:03:17 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Breese",
"John S.",
""
]
] |
1302.6815 | David Heckerman | David Heckerman, Dan Geiger, David Maxwell Chickering | Learning Bayesian Networks: The Combination of Knowledge and Statistical
Data | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-293-301 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe algorithms for learning Bayesian networks from a combination of
user knowledge and statistical data. The algorithms have two components: a
scoring metric and a search procedure. The scoring metric takes a network
structure, statistical data, and a user's prior knowledge, and returns a score
proportional to the posterior probability of the network structure given the
data. The search procedure generates networks for evaluation by the scoring
metric. Our contributions are threefold. First, we identify two important
properties of metrics, which we call event equivalence and parameter
modularity. These properties have been mostly ignored, but when combined,
greatly simplify the encoding of a user's prior knowledge. In particular, a
user can express her knowledge-for the most part-as a single prior Bayesian
network for the domain. Second, we describe local search and annealing
algorithms to be used in conjunction with scoring metrics. In the special case
where each node has at most one parent, we show that heuristic search can be
replaced with a polynomial algorithm to identify the networks with the highest
score. Third, we describe a methodology for evaluating Bayesian-network
learning algorithms. We apply this approach to a comparison of metrics and
search procedures.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:50 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:46:48 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Geiger",
"Dan",
""
],
[
"Chickering",
"David Maxwell",
""
]
] |
1302.6816 | David Heckerman | David Heckerman, Ross D. Shachter | A Decision-Based View of Causality | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-302-310 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most traditional models of uncertainty have focused on the associational
relationship among variables as captured by conditional dependence. In order to
successfully manage intelligent systems for decision making, however, we must
be able to predict the effects of actions. In this paper, we attempt to unite
two branches of research that address such predictions: causal modeling and
decision analysis. First, we provide a definition of causal dependence in
decision-analytic terms, which we derive from consequences of causal dependence
cited in the literature. Using this definition, we show how causal dependence
can be represented within an influence diagram. In particular, we identify two
inadequacies of an ordinary influence diagram as a representation for cause. We
introduce a special class of influence diagrams, called causal influence
diagrams, which corrects one of these problems, and identify situations where
the other inadequacy can be eliminated. In addition, we describe the
relationships between Howard Canonical Form and existing graphical
representations of cause.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:16:56 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:48:17 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1302.6817 | Jochen Heinsohn | Jochen Heinsohn | Probabilistic Description Logics | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-311-318 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | On the one hand, classical terminological knowledge representation excludes
the possibility of handling uncertain concept descriptions involving, e.g.,
"usually true" concept properties, generalized quantifiers, or exceptions. On
the other hand, purely numerical approaches for handling uncertainty in general
are unable to consider terminological knowledge. This paper presents the
language ACP which is a probabilistic extension of terminological logics and
aims at closing the gap between the two areas of research. We present the
formal semantics underlying the language ALUP and introduce the probabilistic
formalism that is based on classes of probabilities and is realized by means of
probabilistic constraints. Besides inferring implicitly existent probabilistic
relationships, the constraints guarantee terminological and probabilistic
consistency. Altogether, the new language ALUP applies to domains where both
term descriptions and uncertainty have to be handled.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:01 GMT"
}
] | 1,362,009,600,000 | [
[
"Heinsohn",
"Jochen",
""
]
] |
1302.6818 | Max Henrion | Max Henrion, Gregory M. Provan, Brendan del Favero, Gillian Sanders | An Experimental Comparison of Numerical and Qualitative Probabilistic
Reasoning | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-319-326 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative and infinitesimal probability schemes are consistent with the
axioms of probability theory, but avoid the need for precise numerical
probabilities. Using qualitative probabilities could substantially reduce the
effort for knowledge engineering and improve the robustness of results. We
examine experimentally how well infinitesimal probabilities (the kappa-calculus
of Goldszmidt and Pearl) perform a diagnostic task - troubleshooting a car that
will not start by comparison with a conventional numerical belief network. We
found the infinitesimal scheme to be as good as the numerical scheme in
identifying the true fault. The performance of the infinitesimal scheme worsens
significantly for prior fault probabilities greater than 0.03. These results
suggest that infinitesimal probability methods may be of substantial practical
value for machine diagnosis with small prior fault probabilities.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:07 GMT"
}
] | 1,362,009,600,000 | [
[
"Henrion",
"Max",
""
],
[
"Provan",
"Gregory M.",
""
],
[
"del Favero",
"Brendan",
""
],
[
"Sanders",
"Gillian",
""
]
] |
1302.6819 | Bernhard Hollunder | Bernhard Hollunder | An Alternative Proof Method for Possibilistic Logic and its Application
to Terminological Logics | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-327-335 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic logic, an extension of first-order logic, deals with
uncertainty that can be estimated in terms of possibility and necessity
measures. Syntactically, this means that a first-order formula is equipped with
a possibility degree or a necessity degree that expresses to what extent the
formula is possibly or necessarily true. Possibilistic resolution yields a
calculus for possibilistic logic which respects the semantics developed for
possibilistic logic. A drawback, which possibilistic resolution inherits from
classical resolution, is that it may not terminate if applied to formulas
belonging to decidable fragments of first-order logic. Therefore we propose an
alternative proof method for possibilistic logic. The main feature of this
method is that it completely abstracts from a concrete calculus but uses as
basic operation a test for classical entailment. We then instantiate
possibilistic logic with a terminological logic, which is a decidable subclass
o f first-order logic but nevertheless much more expressive than propositional
logic. This yields an extension of terminological logics towards the
representation of uncertain knowledge which is satisfactory from a semantic as
well as algorithmic point of view.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:13 GMT"
}
] | 1,362,009,600,000 | [
[
"Hollunder",
"Bernhard",
""
]
] |
1302.6820 | Yen-Teh Hsia | Yen-Teh Hsia | Possibilistic Conditioning and Propagation | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-336-343 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give an axiomatization of confidence transfer - a known conditioning
scheme - from the perspective of expectation-based inference in the sense of
Gardenfors and Makinson. Then, we use the notion of belief independence to
"filter out" different proposal s of possibilistic conditioning rules, all are
variations of confidence transfer. Among the three rules that we consider, only
Dempster's rule of conditioning passes the test of supporting the notion of
belief independence. With the use of this conditioning rule, we then show that
we can use local computation for computing desired conditional marginal
possibilities of the joint possibility satisfying the given constraints. It
turns out that our local computation scheme is already proposed by Shenoy.
However, our intuitions are completely different from that of Shenoy. While
Shenoy just defines a local computation scheme that fits his framework of
valuation-based systems, we derive that local computation scheme from II(,8) =
tI(,8 I a) * II(a) and appropriate independence assumptions, just like how the
Bayesians derive their local computation scheme.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:19 GMT"
}
] | 1,362,009,600,000 | [
[
"Hsia",
"Yen-Teh",
""
]
] |
1302.6821 | Marcus J. Huber | Marcus J. Huber, Edmund H. Durfee, Michael P. Wellman | The Automated Mapping of Plans for Plan Recognition | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-344-351 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To coordinate with other agents in its environment, an agent needs models of
what the other agents are trying to do. When communication is impossible or
expensive, this information must be acquired indirectly via plan recognition.
Typical approaches to plan recognition start with a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:25 GMT"
}
] | 1,362,009,600,000 | [
[
"Huber",
"Marcus J.",
""
],
[
"Durfee",
"Edmund H.",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1302.6822 | Manfred Jaeger | Manfred Jaeger | A Logic for Default Reasoning About Probabilities | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-352-359 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A logic is defined that allows to express information about statistical
probabilities and about degrees of belief in specific propositions. By
interpreting the two types of probabilities in one common probability space,
the semantics given are well suited to model the influence of statistical
information on the formation of subjective beliefs. Cross entropy minimization
is a key element in these semantics, the use of which is justified by showing
that the resulting logic exhibits some very reasonable properties.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:31 GMT"
}
] | 1,362,009,600,000 | [
[
"Jaeger",
"Manfred",
""
]
] |
1302.6823 | Finn Verner Jensen | Finn Verner Jensen, Frank Jensen | Optimal Junction Trees | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-360-366 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper deals with optimality issues in connection with updating beliefs in
networks. We address two processes: triangulation and construction of junction
trees. In the first part, we give a simple algorithm for constructing an
optimal junction tree from a triangulated network. In the second part, we argue
that any exact method based on local calculations must either be less efficient
than the junction tree method, or it has an optimality problem equivalent to
that of triangulation.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:36 GMT"
}
] | 1,362,009,600,000 | [
[
"Jensen",
"Finn Verner",
""
],
[
"Jensen",
"Frank",
""
]
] |
1302.6824 | Frank Jensen | Frank Jensen, Finn Verner Jensen, Soren L. Dittmer | From Influence Diagrams to Junction Trees | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-367-373 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an approach to the solution of decision problems formulated as
influence diagrams. This approach involves a special triangulation of the
underlying graph, the construction of a junction tree with special properties,
and a message passing algorithm operating on the junction tree for computation
of expected utilities and optimal decision policies.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:42 GMT"
}
] | 1,362,009,600,000 | [
[
"Jensen",
"Frank",
""
],
[
"Jensen",
"Finn Verner",
""
],
[
"Dittmer",
"Soren L.",
""
]
] |
1302.6825 | Uffe Kj{\ae}rulff | Uffe Kj{\ae}rulff | Reduction of Computational Complexity in Bayesian Networks through
Removal of Weak Dependencies | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-374-382 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper presents a method for reducing the computational complexity of
Bayesian networks through identification and removal of weak dependencies
(removal of links from the (moralized) independence graph). The removal of a
small number of links may reduce the computational complexity dramatically,
since several fill-ins and moral links may be rendered superfluous by the
removal. The method is described in terms of impact on the independence graph,
the junction tree, and the potential functions associated with these. An
empirical evaluation of the method using large real-world networks demonstrates
the applicability of the method. Further, the method, which has been
implemented in Hugin, complements the approximation method suggested by Jensen
& Andersen (1990).
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:48 GMT"
}
] | 1,362,009,600,000 | [
[
"Kjærulff",
"Uffe",
""
]
] |
1302.6826 | Wai Lam | Wai Lam, Fahiem Bacchus | Using New Data to Refine a Bayesian Network | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-383-390 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore the issue of refining an existent Bayesian network structure using
new data which might mention only a subset of the variables. Most previous
works have only considered the refinement of the network's conditional
probability parameters, and have not addressed the issue of refining the
network's structure. We develop a new approach for refining the network's
structure. Our approach is based on the Minimal Description Length (MDL)
principle, and it employs an adapted version of a Bayesian network learning
algorithm developed in our previous work. One of the adaptations required is to
modify the previous algorithm to account for the structure of the existent
network. The learning algorithm generates a partial network structure which can
then be used to improve the existent network. We also present experimental
evidence demonstrating the effectiveness of our approach.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:17:54 GMT"
}
] | 1,362,009,600,000 | [
[
"Lam",
"Wai",
""
],
[
"Bacchus",
"Fahiem",
""
]
] |
1302.6827 | Jerome Lang | Jerome Lang | Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-391-398 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We view the syntax-based approaches to default reasoning as a model-based
diagnosis problem, where each source giving a piece of information is
considered as a component. It is formalized in the ATMS framework (each source
corresponds to an assumption). We assume then that all sources are independent
and "fail" with a very small probability. This leads to a probability
assignment on the set of candidates, or equivalently on the set of consistent
environments. This probability assignment induces a Dempster-Shafer belief
function which measures the probability that a proposition can be deduced from
the evidence. This belief function can be used in several different ways to
define a non-monotonic consequence relation. We study and compare these
consequence relations. The -case of prioritized knowledge bases is briefly
considered.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:00 GMT"
}
] | 1,362,009,600,000 | [
[
"Lang",
"Jerome",
""
]
] |
1302.6829 | Stephane Lapointe | Stephane Lapointe, Rene Proulx | Fuzzy Geometric Relations to Represent Hierarchical Spatial Information | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-407-415 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A model to represent spatial information is presented in this paper. It is
based on fuzzy constraints represented as fuzzy geometric relations that can be
hierarchically structured. The concept of spatial template is introduced to
capture the idea of interrelated objects in two-dimensional space. The
representation model is used to specify imprecise or vague information
consisting in relative locations and orientations of template objects. It is
shown in this paper how a template represented by this model can be matched
against a crisp situation to recognize a particular instance of this template.
Furthermore, the proximity measure (fuzzy measure) between the instance and the
template is worked out - this measure can be interpreted as a degree of
similarity. In this context, template recognition can be viewed as a case of
fuzzy pattern recognition. The results of this work have been implemented and
applied to a complex military problem from which this work originated.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:12 GMT"
}
] | 1,362,009,600,000 | [
[
"Lapointe",
"Stephane",
""
],
[
"Proulx",
"Rene",
""
]
] |
1302.6830 | Paul E. Lehner | Paul E. Lehner, Christopher Elsaesser, Scott A. Musman | Constructing Belief Networks to Evaluate Plans | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-416-422 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the problem of constructing belief networks to evaluate
plans produced by an knowledge-based planner. Techniques are presented for
handling various types of complicating plan features. These include plans with
context-dependent consequences, indirect consequences, actions with
preconditions that must be true during the execution of an action,
contingencies, multiple levels of abstraction multiple execution agents with
partially-ordered and temporally overlapping actions, and plans which reference
specific times and time durations.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:18 GMT"
}
] | 1,362,009,600,000 | [
[
"Lehner",
"Paul E.",
""
],
[
"Elsaesser",
"Christopher",
""
],
[
"Musman",
"Scott A.",
""
]
] |
1302.6831 | Todd Michael Mansell | Todd Michael Mansell, Grahame Smith | Operator Selection While Planning Under Uncertainty | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-423-431 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes the best first search strategy used by U-Plan (Mansell
1993a), a planning system that constructs quantitatively ranked plans given an
incomplete description of an uncertain environment. U-Plan uses uncertain and
incomplete evidence de scribing the environment, characterizes it using a
Dempster-Shafer interval, and generates a set of possible world states. Plan
construction takes place in an abstraction hierarchy where strategic decisions
are made before tactical decisions. Search through this abstraction hierarchy
is guided by a quantitative measure (expected fulfillment) based on decision
theory. The search strategy is best first with the provision to update expected
fulfillment and review previous decisions in the light of planning
developments. U-Plan generates multiple plans for multiple possible worlds, and
attempts to use existing plans for new world situations. A super-plan is then
constructed, based on merging the set of plans and appropriately timed
knowledge acquisition operators, which are used to decide between plan
alternatives during plan execution.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:23 GMT"
}
] | 1,362,009,600,000 | [
[
"Mansell",
"Todd Michael",
""
],
[
"Smith",
"Grahame",
""
]
] |
1302.6832 | Wolfgang Nejdl | Wolfgang Nejdl, Johann Gamper | Model-Based Diagnosis with Qualitative Temporal Uncertainty | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-432-439 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we describe a framework for model-based diagnosis of dynamic
systems, which extends previous work in this field by using and expressing
temporal uncertainty in the form of qualitative interval relations a la Allen.
Based on a logical framework extended by qualitative and quantitative temporal
constraints we show how to describe behavioral models (both consistency- and
abductive-based), discuss how to use abstract observations and show how
abstract temporal diagnoses are computed. This yields an expressive framework,
which allows the representation of complex temporal behavior allowing us to
represent temporal uncertainty. Due to its abstraction capabilities computation
is made independent of the number of observations and time points in a temporal
setting. An example of hepatitis diagnosis is used throughout the paper.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:29 GMT"
}
] | 1,362,009,600,000 | [
[
"Nejdl",
"Wolfgang",
""
],
[
"Gamper",
"Johann",
""
]
] |
1302.6833 | Keung-Chi Ng | Keung-Chi Ng, Tod S. Levitt | Incremental Dynamic Construction of Layered Polytree Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-440-446 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Certain classes of problems, including perceptual data understanding,
robotics, discovery, and learning, can be represented as incremental,
dynamically constructed belief networks. These automatically constructed
networks can be dynamically extended and modified as evidence of new
individuals becomes available. The main result of this paper is the incremental
extension of the singly connected polytree network in such a way that the
network retains its singly connected polytree structure after the changes. The
algorithm is deterministic and is guaranteed to have a complexity of single
node addition that is at most of order proportional to the number of nodes (or
size) of the network. Additional speed-up can be achieved by maintaining the
path information. Despite its incremental and dynamic nature, the algorithm can
also be used for probabilistic inference in belief networks in a fashion
similar to other exact inference algorithms.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:35 GMT"
}
] | 1,362,009,600,000 | [
[
"Ng",
"Keung-Chi",
""
],
[
"Levitt",
"Tod S.",
""
]
] |
1302.6835 | Judea Pearl | Judea Pearl | A Probabilistic Calculus of Actions | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-454-462 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a symbolic machinery that admits both probabilistic and causal
information about a given domain and produces probabilistic statements about
the effect of actions and the impact of observations. The calculus admits two
types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which
represents the observation X = x, and causal conditioning, P(y|do(X = x)), read
the probability of Y = y conditioned on holding X constant (at x) by deliberate
action. Given a mixture of such observational and causal sentences, together
with the topology of the causal graph, the calculus derives new conditional
probabilities of both types, thus enabling one to quantify the effects of
actions (and policies) from partially specified knowledge bases, such as
Bayesian networks in which some conditional probabilities may not be available.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:47 GMT"
}
] | 1,362,009,600,000 | [
[
"Pearl",
"Judea",
""
]
] |
1302.6836 | Stephen G. Pimentel | Stephen G. Pimentel, Lawrence M. Brem | Robust Planning in Uncertain Environments | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-463-469 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a novel approach to planning which takes advantage of
decision theory to greatly improve robustness in an uncertain environment. We
present an algorithm which computes conditional plans of maximum expected
utility. This algorithm relies on a representation of the search space as an
AND/OR tree and employs a depth-limit to control computation costs. A numeric
robustness factor, which parameterizes the utility function, allows the user to
modulate the degree of risk-aversion employed by the planner. Via a look-ahead
search, the planning algorithm seeks to find an optimal plan using expected
utility as its optimization criterion. We present experimental results obtained
by applying our algorithm to a non-deterministic extension of the blocks world
domain. Our results demonstrate that the robustness factor governs the degree
of risk embodied in the conditional plans computed by our algorithm.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:53 GMT"
}
] | 1,362,009,600,000 | [
[
"Pimentel",
"Stephen G.",
""
],
[
"Brem",
"Lawrence M.",
""
]
] |
1302.6837 | Michael Pittarelli | Michael Pittarelli | Anytime Decision Making with Imprecise Probabilities | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-470-477 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines methods of decision making that are able to accommodate
limitations on both the form in which uncertainty pertaining to a decision
problem can be realistically represented and the amount of computing time
available before a decision must be made. The methods are anytime algorithms in
the sense of Boddy and Dean 1991. Techniques are presented for use with Frisch
and Haddawy's [1992] anytime deduction system, with an anytime adaptation of
Nilsson's [1986] probabilistic logic, and with a probabilistic database model.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:18:58 GMT"
}
] | 1,362,009,600,000 | [
[
"Pittarelli",
"Michael",
""
]
] |
1302.6839 | Malcolm Pradhan | Malcolm Pradhan, Gregory M. Provan, Blackford Middleton, Max Henrion | Knowledge Engineering for Large Belief Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-484-490 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present several techniques for knowledge engineering of large belief
networks (BNs) based on the our experiences with a network derived from a large
medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is
used to model causal in dependence in a BN with multi-valued variables. We
describe the use of leak probabilities to enforce the closed-world assumption
in our model. We present Netview, a visualization tool based on causal
independence and the use of leak probabilities. The Netview software allows
knowledge engineers to dynamically view sub-networks for knowledge engineering,
and it provides version control for editing a BN. Netview generates
sub-networks in which leak probabilities are dynamically updated to reflect the
missing portions of the network.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:10 GMT"
}
] | 1,362,009,600,000 | [
[
"Pradhan",
"Malcolm",
""
],
[
"Provan",
"Gregory M.",
""
],
[
"Middleton",
"Blackford",
""
],
[
"Henrion",
"Max",
""
]
] |
1302.6840 | Runping Qi | Runping Qi, Nevin Lianwen Zhang, David L. Poole | Solving Asymmetric Decision Problems with Influence Diagrams | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-491-497 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While influence diagrams have many advantages as a representation framework
for Bayesian decision problems, they have a serious drawback in handling
asymmetric decision problems. To be represented in an influence diagram, an
asymmetric decision problem must be symmetrized. A considerable amount of
unnecessary computation may be involved when a symmetrized influence diagram is
evaluated by conventional algorithms. In this paper we present an approach for
avoiding such unnecessary computation in influence diagram evaluation.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:16 GMT"
}
] | 1,362,009,600,000 | [
[
"Qi",
"Runping",
""
],
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Poole",
"David L.",
""
]
] |
1302.6841 | Marco Ramoni | Marco Ramoni, Alberto Riva | Belief Maintenance in Bayesian Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-498-505 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning under
uncertainty but bear some severe limitations: they require a large amount of
information before any reasoning process can start, they have limited
contradiction handling capabilities, and their ability to provide explanations
for their conclusion is still controversial. There exists a class of reasoning
systems, called Truth Maintenance Systems (TMSs), which are able to deal with
partially specified knowledge, to provide well-founded explanation for their
conclusions, and to detect and handle contradictions. TMSs incorporating
measure of uncertainty are called Belief Maintenance Systems (BMSs). This paper
describes how a BMS based on probabilistic logic can be applied to BBNs, thus
introducing a new class of BBNs, called Ignorant Belief Networks, able to
incrementally deal with partially specified conditional dependencies, to
provide explanations, and to detect and handle contradictions.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:22 GMT"
}
] | 1,362,009,600,000 | [
[
"Ramoni",
"Marco",
""
],
[
"Riva",
"Alberto",
""
]
] |
1302.6842 | Eugene Santos Jr. | Eugene Santos Jr., Solomon Eyal Shimony | Belief Updating by Enumerating High-Probability Independence-Based
Assignments | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-506-513 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Independence-based (IB) assignments to Bayesian belief networks were
originally proposed as abductive explanations. IB assignments assign fewer
variables in abductive explanations than do schemes assigning values to all
evidentially supported variables. We use IB assignments to approximate marginal
probabilities in Bayesian belief networks. Recent work in belief updating for
Bayes networks attempts to approximate posterior probabilities by finding a
small number of the highest probability complete (or perhaps evidentially
supported) assignments. Under certain assumptions, the probability mass in the
union of these assignments is sufficient to obtain a good approximation. Such
methods are especially useful for highly-connected networks, where the maximum
clique size or the cutset size make the standard algorithms intractable. Since
IB assignments contain fewer assigned variables, the probability mass in each
assignment is greater than in the respective complete assignment. Thus, fewer
IB assignments are sufficient, and a good approximation can be obtained more
efficiently. IB assignments can be used for efficiently approximating posterior
node probabilities even in cases which do not obey the rather strict skewness
assumptions used in previous research. Two algorithms for finding the high
probability IB assignments are suggested: one by doing a best-first heuristic
search, and another by special-purpose integer linear programming. Experimental
results show that this approach is feasible for highly connected belief
networks.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:28 GMT"
}
] | 1,362,009,600,000 | [
[
"Santos",
"Eugene",
"Jr."
],
[
"Shimony",
"Solomon Eyal",
""
]
] |
1302.6843 | Ross D. Shachter | Ross D. Shachter, Stig K. Andersen, Peter Szolovits | Global Conditioning for Probabilistic Inference in Belief Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-514-522 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a new approach to probabilistic inference on belief
networks, global conditioning, which is a simple generalization of Pearl's
(1986b) method of loopcutset conditioning. We show that global conditioning, as
well as loop-cutset conditioning, can be thought of as a special case of the
method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (199Oa;
1990b). Nonetheless, this approach provides new opportunities for parallel
processing and, in the case of sequential processing, a tradeoff of time for
memory. We also show how a hybrid method (Suermondt and others 1990) combining
loop-cutset conditioning with Jensen's method can be viewed within our
framework. By exploring the relationships between these methods, we develop a
unifying framework in which the advantages of each approach can be combined
successfully.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:34 GMT"
}
] | 1,362,009,600,000 | [
[
"Shachter",
"Ross D.",
""
],
[
"Andersen",
"Stig K.",
""
],
[
"Szolovits",
"Peter",
""
]
] |
1302.6844 | Philippe Smets | Philippe Smets | Belief Induced by the Partial Knowledge of the Probabilities | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-523-532 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We construct the belief function that quantifies the agent, beliefs about
which event of Q will occurred when he knows that the event is selected by a
chance set-up and that the probability function associated to the chance set up
is only partially known.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:41 GMT"
}
] | 1,362,009,600,000 | [
[
"Smets",
"Philippe",
""
]
] |
1302.6845 | Paul Snow | Paul Snow | Ignorance and the Expressiveness of Single- and Set-Valued Probability
Models of Belief | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-531-537 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over time, there have hen refinements in the way that probability
distributions are used for representing beliefs. Models which rely on single
probability distributions depict a complete ordering among the propositions of
interest, yet human beliefs are sometimes not completely ordered. Non-singleton
sets of probability distributions can represent partially ordered beliefs.
Convex sets are particularly convenient and expressive, but it is known that
there are reasonable patterns of belief whose faithful representation require
less restrictive sets. The present paper shows that prior ignorance about three
or more exclusive alternatives and the emergence of partially ordered beliefs
when evidence is obtained defy representation by any single set of
distributions, but yield to a representation baud on several uts. The partial
order is shown to be a partial qualitative probability which shares some
intuitively appealing attributes with probability distributions.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:46 GMT"
}
] | 1,362,009,600,000 | [
[
"Snow",
"Paul",
""
]
] |
1302.6846 | Sampath Srinivas | Sampath Srinivas | A Probabilistic Approach to Hierarchical Model-based Diagnosis | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-538-545 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model-based diagnosis reasons backwards from a functional schematic of a
system to isolate faults given observations of anomalous behavior. We develop a
fully probabilistic approach to model based diagnosis and extend it to support
hierarchical models. Our scheme translates the functional schematic into a
Bayesian network and diagnostic inference takes place in the Bayesian network.
A Bayesian network diagnostic inference algorithm is modified to take advantage
of the hierarchy to give computational gains.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:52 GMT"
}
] | 1,362,009,600,000 | [
[
"Srinivas",
"Sampath",
""
]
] |
1302.6847 | Milan Studeny | Milan Studeny | Semigraphoids Are Two-Antecedental Approximations of Stochastic
Conditional Independence Models | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-546-552 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The semigraphoid closure of every couple of CI-statements (GI=conditional
independence) is a stochastic CI-model. As a consequence of this result it is
shown that every probabilistically sound inference rule for CI-model, having at
most two antecedents, is derivable from the semigraphoid inference rules. This
justifies the use of semigraphoids as approximations of stochastic CI-models in
probabilistic reasoning. The list of all 19 potential dominant elements of the
mentioned semigraphoid closure is given as a byproduct.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:19:58 GMT"
}
] | 1,362,009,600,000 | [
[
"Studeny",
"Milan",
""
]
] |
1302.6848 | Sek-Wah Tan | Sek-Wah Tan | Exceptional Subclasses in Qualitative Probability | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-553-559 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | System Z+ [Goldszmidt and Pearl, 1991, Goldszmidt, 1992] is a formalism for
reasoning with normality defaults of the form "typically if phi then + (with
strength cf)" where 6 is a positive integer. The system has a critical
shortcoming in that it does not sanction inheritance across exceptional
subclasses. In this paper we propose an extension to System Z+ that rectifies
this shortcoming by extracting additional conditions between worlds from the
defaults database. We show that the additional constraints do not change the
notion of the consistency of a database. We also make comparisons with
competing default reasoning systems.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:04 GMT"
}
] | 1,362,009,600,000 | [
[
"Tan",
"Sek-Wah",
""
]
] |
1302.6849 | Pei Wang | Pei Wang | A Defect in Dempster-Shafer Theory | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-560-566 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By analyzing the relationships among chance, weight of evidence and degree of
beliefwe show that the assertion "probability functions are special cases of
belief functions" and the assertion "Dempster's rule can be used to combine
belief functions based on distinct bodies of evidence" together lead to an
inconsistency in Dempster-Shafer theory. To solve this problem, we must reject
some fundamental postulates of the theory. We introduce a new approach for
uncertainty management that shares many intuitive ideas with D-S theory, while
avoiding this problem.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:10 GMT"
}
] | 1,362,009,600,000 | [
[
"Wang",
"Pei",
""
]
] |
1302.6850 | Michael P. Wellman | Michael P. Wellman, Chao-Lin Liu | State-space Abstraction for Anytime Evaluation of Probabilistic Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-567-574 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One important factor determining the computational complexity of evaluating a
probabilistic network is the cardinality of the state spaces of the nodes. By
varying the granularity of the state spaces, one can trade off accuracy in the
result for computational efficiency. We present an anytime procedure for
approximate evaluation of probabilistic networks based on this idea. On
application to some simple networks, the procedure exhibits a smooth
improvement in approximation quality as computation time increases. This
suggests that state-space abstraction is one more useful control parameter for
designing real-time probabilistic reasoners.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:17 GMT"
}
] | 1,362,009,600,000 | [
[
"Wellman",
"Michael P.",
""
],
[
"Liu",
"Chao-Lin",
""
]
] |
1302.6851 | Emil Weydert | Emil Weydert | General Belief Measures | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-575-582 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probability measures by themselves, are known to be inappropriate for
modeling the dynamics of plain belief and their excessively strong
measurability constraints make them unsuitable for some representational tasks,
e.g. in the context of firstorder knowledge. In this paper, we are therefore
going to look for possible alternatives and extensions. We begin by delimiting
the general area of interest, proposing a minimal list of assumptions to be
satisfied by any reasonable quasi-probabilistic valuation concept. Within this
framework, we investigate two particularly interesting kinds of quasi-measures
which are not or much less affected by the traditional problems. * Ranking
measures, which generalize Spohn-type and possibility measures. * Cumulative
measures, which combine the probabilistic and the ranking philosophy, allowing
thereby a fine-grained account of static and dynamic belief.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:23 GMT"
}
] | 1,362,009,600,000 | [
[
"Weydert",
"Emil",
""
]
] |
1302.6852 | Nic Wilson | Nic Wilson | Generating Graphoids from Generalised Conditional Probability | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-583-590 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We take a general approach to uncertainty on product spaces, and give
sufficient conditions for the independence structures of uncertainty measures
to satisfy graphoid properties. Since these conditions are arguably more
intuitive than some of the graphoid properties, they can be viewed as
explanations why probability and certain other formalisms generate graphoids.
The conditions include a sufficient condition for the Intersection property
which can still apply even if there is a strong logical relations hip between
the variables. We indicate how these results can be used to produce theories of
qualitative conditional probability which are semi-graphoids and graphoids.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:29 GMT"
}
] | 1,362,009,600,000 | [
[
"Wilson",
"Nic",
""
]
] |
1302.6853 | Michael S. K. M. Wong | Michael S. K. M. Wong, Z. W. Wang | On Axiomatization of Probabilistic Conditional Independencies | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-591-597 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the connection between probabilistic conditional
independence in uncertain reasoning and data dependency in relational
databases. As a demonstration of the usefulness of this preliminary
investigation, an alternate proof is presented for refuting the conjecture
suggested by Pearl and Paz that probabilistic conditional independencies have a
complete axiomatization.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:35 GMT"
}
] | 1,362,009,600,000 | [
[
"Wong",
"Michael S. K. M.",
""
],
[
"Wang",
"Z. W.",
""
]
] |
1302.6854 | Hong Xu | Hong Xu, Philippe Smets | Evidential Reasoning with Conditional Belief Functions | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-598-605 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the existing evidential networks with belief functions, the relations
among the variables are always represented by joint belief functions on the
product space of the involved variables. In this paper, we use conditional
belief functions to represent such relations in the network and show some
relations of these two kinds of representations. We also present a propagation
algorithm for such networks. By analyzing the properties of some special
evidential networks with conditional belief functions, we show that the
reasoning process can be simplified in such kinds of networks.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:41 GMT"
}
] | 1,362,009,600,000 | [
[
"Xu",
"Hong",
""
],
[
"Smets",
"Philippe",
""
]
] |
1302.6855 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, David L Poole | Inter-causal Independence and Heterogeneous Factorization | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-606-614 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well known that conditional independence can be used to factorize a
joint probability into a multiplication of conditional probabilities. This
paper proposes a constructive definition of inter-causal independence, which
can be used to further factorize a conditional probability. An inference
algorithm is developed, which makes use of both conditional independence and
inter-causal independence to reduce inference complexity in Bayesian networks.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:20:47 GMT"
}
] | 1,362,009,600,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Poole",
"David L",
""
]
] |
1303.1454 | Marek J. Druzdzel | Marek J. Druzdzel, Herbert A. Simon | Causality in Bayesian Belief Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-3-11 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of causal interpretation of the graphical structure of
Bayesian belief networks (BBNs). We review the concept of causality explicated
in the domain of structural equations models and show that it is applicable to
BBNs. In this view, which we call mechanism-based, causality is defined within
models and causal asymmetries arise when mechanisms are placed in the context
of a system. We lay the link between structural equations models and BBNs
models and formulate the conditions under which the latter can be given causal
interpretation.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:18:23 GMT"
}
] | 1,362,700,800,000 | [
[
"Druzdzel",
"Marek J.",
""
],
[
"Simon",
"Herbert A.",
""
]
] |
1303.1455 | Judea Pearl | Judea Pearl | From Conditional Oughts to Qualitative Decision Theory | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-12-20 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The primary theme of this investigation is a decision theoretic account of
conditional ought statements (e.g., "You ought to do A, if C") that rectifies
glaring deficiencies in classical deontic logic. The resulting account forms a
sound basis for qualitative decision theory, thus providing a framework for
qualitative planning under uncertainty. In particular, we show that adding
causal relationships (in the form of a single graph) as part of an epistemic
state is sufficient to facilitate the analysis of action sequences, their
consequences, their interaction with observations, their expected utilities
and, hence, the synthesis of plans and strategies under uncertainty.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:18:29 GMT"
}
] | 1,362,700,800,000 | [
[
"Pearl",
"Judea",
""
]
] |
1303.1456 | Russ B. Altman | Russ B. Altman | A Probabilistic Algorithm for Calculating Structure: Borrowing from
Simulated Annealing | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-23-31 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have developed a general Bayesian algorithm for determining the
coordinates of points in a three-dimensional space. The algorithm takes as
input a set of probabilistic constraints on the coordinates of the points, and
an a priori distribution for each point location. The output is a
maximum-likelihood estimate of the location of each point. We use the extended,
iterated Kalman filter, and add a search heuristic for optimizing its solution
under nonlinear conditions. This heuristic is based on the same principle as
the simulated annealing heuristic for other optimization problems. Our method
uses any probabilistic constraints that can be expressed as a function of the
point coordinates (for example, distance, angles, dihedral angles, and
planarity). It assumes that all constraints have Gaussian noise. In this paper,
we describe the algorithm and show its performance on a set of synthetic data
to illustrate its convergence properties, and its applicability to domains such
ng molecular structure determination.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:18:35 GMT"
}
] | 1,362,700,800,000 | [
[
"Altman",
"Russ B.",
""
]
] |
1303.1457 | Scott A. Musman | Scott A. Musman, L. W. Chang | A Study of Scaling Issues in Bayesian Belief Networks for Ship
Classification | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-32-39 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problems associated with scaling involve active and challenging research
topics in the area of artificial intelligence. The purpose is to solve real
world problems by means of AI technologies, in cases where the complexity of
representation of the real world problem is potentially combinatorial. In this
paper, we present a novel approach to cope with the scaling issues in Bayesian
belief networks for ship classification. The proposed approach divides the
conceptual model of a complex ship classification problem into a set of small
modules that work together to solve the classification problem while preserving
the functionality of the original model. The possible ways of explaining sensor
returns (e.g., the evidence) for some features, such as portholes along the
length of a ship, are sometimes combinatorial. Thus, using an exhaustive
approach, which entails the enumeration of all possible explanations, is
impractical for larger problems. We present a network structure (referred to as
Sequential Decomposition, SD) in which each observation is associated with a
set of legitimate outcomes which are consistent with the explanation of each
observed piece of evidence. The results show that the SD approach allows one to
represent feature-observation relations in a manageable way and achieve the
same explanatory power as an exhaustive approach.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:18:41 GMT"
}
] | 1,362,700,800,000 | [
[
"Musman",
"Scott A.",
""
],
[
"Chang",
"L. W.",
""
]
] |
1303.1458 | Gregory M. Provan | Gregory M. Provan | Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-40-47 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the tradeoffs which need to be considered in reasoning
using probabilistic network representations, such as Influence Diagrams (IDs).
In particular, we examine the tradeoffs entailed in using Temporal Influence
Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic
system without prohibitive data and computational requirements. Three
approaches for TID construction which make different tradeoffs are examined:
(1) tailoring the network at each time interval to the data available (rather
then just copying the original Bayes Network for all time intervals); (2)
modeling the evolution of a parsimonious subset of variables (rather than all
variables); and (3) model selection approaches, which seek to minimize some
measure of the predictive accuracy of the model without introducing too many
parameters, which might cause "overfitting" of the model. Methods of evaluating
the accuracy/efficiency of the tradeoffs are proposed.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:18:46 GMT"
}
] | 1,362,700,800,000 | [
[
"Provan",
"Gregory M.",
""
]
] |
1303.1459 | Harold P. Lehmann | Harold P. Lehmann, Ross D. Shachter | End-User Construction of Influence Diagrams for Bayesian Statistics | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-48-54 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influence diagrams are ideal knowledge representations for Bayesian
statistical models. However, these diagrams are difficult for end users to
interpret and to manipulate. We present a user-based architecture that enables
end users to create and to manipulate the knowledge representation. We use the
problem of physicians' interpretation of two-arm parallel randomized clinical
trials (TAPRCT) to illustrate the architecture and its use. There are three
primary data structures. Elements of statistical models are encoded as
subgraphs of a restricted class of influence diagram. The interpretations of
those elements are mapped into users' language in a domain-specific, user-based
semantic interface, called a patient-flow diagram, in the TAPRCT problem.
Pennitted transformations of the statistical model that maintain the semantic
relationships of the model are encoded in a metadata-state diagram, called the
cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system
uses modular actions called construction steps. This framework has been
implemented in a system called THOMAS, that allows physicians to interpret the
data reported from a TAPRCT.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:18:52 GMT"
}
] | 1,362,700,800,000 | [
[
"Lehmann",
"Harold P.",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1303.1461 | Paul Dagum | Paul Dagum, Adam Galper | Forecasting Sleep Apnea with Dynamic Network Models | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-64-71 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamic network models (DNMs) are belief networks for temporal reasoning. The
DNM methodology combines techniques from time series analysis and probabilistic
reasoning to provide (1) a knowledge representation that integrates
noncontemporaneous and contemporaneous dependencies and (2) methods for
iteratively refining these dependencies in response to the effects of exogenous
influences. We use belief-network inference algorithms to perform forecasting,
control, and discrete event simulation on DNMs. The belief network formulation
allows us to move beyond the traditional assumptions of linearity in the
relationships among time-dependent variables and of normality in their
probability distributions. We demonstrate the DNM methodology on an important
forecasting problem in medicine. We conclude with a discussion of how the
methodology addresses several limitations found in traditional time series
analyses.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:03 GMT"
}
] | 1,362,700,800,000 | [
[
"Dagum",
"Paul",
""
],
[
"Galper",
"Adam",
""
]
] |
1303.1462 | Peter J. Regan | Peter J. Regan | Normative Engineering Risk Management Systems | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-72-79 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a normative system design that incorporates diagnosis,
dynamic evolution, decision making, and information gathering. A single
influence diagram demonstrates the design's coherence, yet each activity is
more effectively modeled and evaluated separately. Application to offshore oil
platforms illustrates the design. For this application, the normative system is
embedded in a real-time expert system.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:10 GMT"
}
] | 1,362,700,800,000 | [
[
"Regan",
"Peter J.",
""
]
] |
1303.1463 | David Heckerman | David Heckerman, Michael Shwe | Diagnosis of Multiple Faults: A Sensitivity Analysis | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-80-87 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We compare the diagnostic accuracy of three diagnostic inference models: the
simple Bayes model, the multimembership Bayes model, which is isomorphic to the
parallel combination function in the certainty-factor model, and a model that
incorporates the noisy OR-gate interaction. The comparison is done on 20
clinicopathological conference (CPC) cases from the American Journal of
Medicine-challenging cases describing actual patients often with multiple
disorders. We find that the distributions produced by the noisy OR model agree
most closely with the gold-standard diagnoses, although substantial differences
exist between the distributions and the diagnoses. In addition, we find that
the multimembership Bayes model tends to significantly overestimate the
posterior probabilities of diseases, whereas the simple Bayes model tends to
significantly underestimate the posterior probabilities. Our results suggest
that additional work to refine the noisy OR model for internal medicine will be
worthwhile.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:15 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:52:47 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Shwe",
"Michael",
""
]
] |
1303.1464 | Paul Dagum | Paul Dagum, Adam Galper | Additive Belief-Network Models | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-91-98 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The inherent intractability of probabilistic inference has hindered the
application of belief networks to large domains. Noisy OR-gates [30] and
probabilistic similarity networks [18, 17] escape the complexity of inference
by restricting model expressiveness. Recent work in the application of
belief-network models to time-series analysis and forecasting [9, 10] has given
rise to the additive belief network model (ABNM). We (1) discuss the nature and
implications of the approximations made by an additive decomposition of a
belief network, (2) show greater efficiency in the induction of additive models
when available data are scarce, (3) generalize probabilistic inference
algorithms to exploit the additive decomposition of ABNMs, (4) show greater
efficiency of inference, and (5) compare results on inference with a simple
additive belief network.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:21 GMT"
}
] | 1,362,700,800,000 | [
[
"Dagum",
"Paul",
""
],
[
"Galper",
"Adam",
""
]
] |
1303.1465 | Francisco Javier Diez | Francisco Javier Diez | Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-99-105 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian
networks and proposed three models for the representation of conditional
probabilities. A forth model, shown here, assumes that the parameter
distribution is given by a product of Gaussian functions and updates them from
the _ and _r messages of evidence propagation. We also generalize the noisy
OR-gate for multivalued variables, develop the algorithm to compute probability
in time proportional to the number of parents (even in networks with loops) and
apply the learning model to this gate.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:27 GMT"
}
] | 1,362,700,800,000 | [
[
"Diez",
"Francisco Javier",
""
]
] |
1303.1466 | Didier Dubois | Didier Dubois, Henri Prade | A fuzzy relation-based extension of Reggia's relational model for
diagnosis handling uncertain and incomplete information | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-106-113 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relational models for diagnosis are based on a direct description of the
association between disorders and manifestations. This type of model has been
specially used and developed by Reggia and his co-workers in the late eighties
as a basic starting point for approaching diagnosis problems. The paper
proposes a new relational model which includes Reggia's model as a particular
case and which allows for a more expressive representation of the observations
and of the manifestations associated with disorders. The model distinguishes,
i) between manifestations which are certainly absent and those which are not
(yet) observed, and ii) between manifestations which cannot be caused by a
given disorder and manifestations for which we do not know if they can or
cannot be caused by this disorder. This new model, which can handle uncertainty
in a non-probabilistic way, is based on possibility theory and so-called
twofold fuzzy sets, previously introduced by the authors.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:33 GMT"
}
] | 1,362,700,800,000 | [
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1303.1467 | Morten Elvang-G{\o}ransson | Morten Elvang-G{\o}ransson, Paul J. Krause, John Fox | Dialectic Reasoning with Inconsistent Information | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-114-121 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From an inconsistent database non-trivial arguments may be constructed both
for a proposition, and for the contrary of that proposition. Therefore,
inconsistency in a logical database causes uncertainty about which conclusions
to accept. This kind of uncertainty is called logical uncertainty. We define a
concept of "acceptability", which induces a means for differentiating
arguments. The more acceptable an argument, the more confident we are in it. A
specific interest is to use the acceptability classes to assign linguistic
qualifiers to propositions, such that the qualifier assigned to a propositions
reflects its logical uncertainty. A more general interest is to understand how
classes of acceptability can be defined for arguments constructed from an
inconsistent database, and how this notion of acceptability can be devised to
reflect different criteria. Whilst concentrating on the aspects of assigning
linguistic qualifiers to propositions, we also indicate the more general
significance of the notion of acceptability.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:38 GMT"
}
] | 1,376,265,600,000 | [
[
"Elvang-Gøransson",
"Morten",
""
],
[
"Krause",
"Paul J.",
""
],
[
"Fox",
"John",
""
]
] |
1303.1468 | David Heckerman | David Heckerman | Causal Independence for Knowledge Acquisition and Inference | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-122-127 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I introduce a temporal belief-network representation of causal independence
that a knowledge engineer can use to elicit probabilistic models. Like the
current, atemporal belief-network representation of causal independence, the
new representation makes knowledge acquisition tractable. Unlike the atemproal
representation, however, the temporal representation can simplify inference,
and does not require the use of unobservable variables. The representation is
less general than is the atemporal representation, but appears to be useful for
many practical applications.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:44 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:51:05 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
]
] |
1303.1469 | Eric J. Horvitz | Eric J. Horvitz, Adrian Klein | Utility-Based Abstraction and Categorization | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-128-135 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We take a utility-based approach to categorization. We construct
generalizations about events and actions by considering losses associated with
failing to distinguish among detailed distinctions in a decision model. The
utility-based methods transform detailed states of the world into more abstract
categories comprised of disjunctions of the states. We show how we can cluster
distinctions into groups of distinctions at progressively higher levels of
abstraction, and describe rules for decision making with the abstractions. The
techniques introduce a utility-based perspective on the nature of concepts, and
provide a means of simplifying decision models used in automated reasoning
systems. We demonstrate the techniques by describing the capabilities and
output of TUBA, a program for utility-based abstraction.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:50 GMT"
}
] | 1,362,700,800,000 | [
[
"Horvitz",
"Eric J.",
""
],
[
"Klein",
"Adrian",
""
]
] |
1303.1470 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey | Sensitivity Analysis for Probability Assessments in Bayesian Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-136-142 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When eliciting probability models from experts, knowledge engineers may
compare the results of the model with expert judgment on test scenarios, then
adjust model parameters to bring the behavior of the model more in line with
the expert's intuition. This paper presents a methodology for analytic
computation of sensitivity values to measure the impact of small changes in a
network parameter on a target probability value or distribution. These values
can be used to guide knowledge elicitation. They can also be used in a gradient
descent algorithm to estimate parameter values that maximize a measure of
goodness-of-fit to both local and holistic probability assessments.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:19:56 GMT"
}
] | 1,362,700,800,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1303.1471 | John F. Lemmer | John F. Lemmer | Causal Modeling | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-143-151 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal Models are like Dependency Graphs and Belief Nets in that they provide
a structure and a set of assumptions from which a joint distribution can, in
principle, be computed. Unlike Dependency Graphs, Causal Models are models of
hierarchical and/or parallel processes, rather than models of distributions
(partially) known to a model builder through some sort of gestalt. As such,
Causal Models are more modular, easier to build, more intuitive, and easier to
understand than Dependency Graph Models. Causal Models are formally defined and
Dependency Graph Models are shown to be a special case of them. Algorithms
supporting inference are presented. Parsimonious methods for eliciting
dependent probabilities are presented.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:02 GMT"
}
] | 1,362,700,800,000 | [
[
"Lemmer",
"John F.",
""
]
] |
1303.1472 | Izhar Matzkevich | Izhar Matzkevich, Bruce Abramson | Some Complexity Considerations in the Combination of Belief Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-152-158 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One topic that is likely to attract an increasing amount of attention within
the Knowledge-base systems research community is the coordination of
information provided by multiple experts. We envision a situation in which
several experts independently encode information as belief networks. A
potential user must then coordinate the conclusions and recommendations of
these networks to derive some sort of consensus. One approach to such a
consensus is the fusion of the contributed networks into a single, consensus
model prior to the consideration of any case-specific data (specific
observations, test results). This approach requires two types of combination
procedures, one for probabilities, and one for graphs. Since the combination of
probabilities is relatively well understood, the key barriers to this approach
lie in the realm of graph theory. This paper provides formal definitions of
some of the operations necessary to effect the necessary graphical
combinations, and provides complexity analyses of these procedures. The paper's
key result is that most of these operations are NP-hard, and its primary
message is that the derivation of ?good? consensus networks must be done
heuristically.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:07 GMT"
}
] | 1,362,700,800,000 | [
[
"Matzkevich",
"Izhar",
""
],
[
"Abramson",
"Bruce",
""
]
] |
1303.1473 | Izhar Matzkevich | Izhar Matzkevich, Bruce Abramson | Deriving a Minimal I-map of a Belief Network Relative to a Target
Ordering of its Nodes | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-159-165 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper identifies and solves a new optimization problem: Given a belief
network (BN) and a target ordering on its variables, how can we efficiently
derive its minimal I-map whose arcs are consistent with the target ordering? We
present three solutions to this problem, all of which lead to directed acyclic
graphs based on the original BN's recursive basis relative to the specified
ordering (such a DAG is sometimes termed the boundary DAG drawn from the given
BN relative to the said ordering [5]). Along the way, we also uncover an
important general principal about arc reversals: when reordering a BN according
to some target ordering, (while attempting to minimize the number of arcs
generated), the sequence of arc reversals should follow the topological
ordering induced by the original belief network's arcs to as great an extent as
possible. These results promise to have a significant impact on the derivation
of consensus models, as well as on other algorithms that require the
reconfiguration and/or combination of BN's.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:12 GMT"
}
] | 1,362,700,800,000 | [
[
"Matzkevich",
"Izhar",
""
],
[
"Abramson",
"Bruce",
""
]
] |
1303.1474 | Kim-Leng Poh | Kim-Leng Poh, Michael R. Fehling | Probabilistic Conceptual Network: A Belief Representation Scheme for
Utility-Based Categorization | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-166-173 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic conceptual network is a knowledge representation scheme
designed for reasoning about concepts and categorical abstractions in
utility-based categorization. The scheme combines the formalisms of abstraction
and inheritance hierarchies from artificial intelligence, and probabilistic
networks from decision analysis. It provides a common framework for
representing conceptual knowledge, hierarchical knowledge, and uncertainty. It
facilitates dynamic construction of categorization decision models at varying
levels of abstraction. The scheme is applied to an automated machining problem
for reasoning about the state of the machine at varying levels of abstraction
in support of actions for maintaining competitiveness of the plant.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:18 GMT"
}
] | 1,362,700,800,000 | [
[
"Poh",
"Kim-Leng",
""
],
[
"Fehling",
"Michael R.",
""
]
] |
1303.1475 | Kim-Leng Poh | Kim-Leng Poh, Eric J. Horvitz | Reasoning about the Value of Decision-Model Refinement: Methods and
Application | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-174-182 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the value of extending the completeness of a decision model
along different dimensions of refinement. Specifically, we analyze the expected
value of quantitative, conceptual, and structural refinement of decision
models. We illustrate the key dimensions of refinement with examples. The
analyses of value of model refinement can be used to focus the attention of an
analyst or an automated reasoning system on extensions of a decision model
associated with the greatest expected value.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:23 GMT"
}
] | 1,362,700,800,000 | [
[
"Poh",
"Kim-Leng",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1303.1476 | William B. Poland | William B. Poland, Ross D. Shachter | Mixtures of Gaussians and Minimum Relative Entropy Techniques for
Modeling Continuous Uncertainties | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-183-190 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Problems of probabilistic inference and decision making under uncertainty
commonly involve continuous random variables. Often these are discretized to a
few points, to simplify assessments and computations. An alternative
approximation is to fit analytically tractable continuous probability
distributions. This approach has potential simplicity and accuracy advantages,
especially if variables can be transformed first. This paper shows how a
minimum relative entropy criterion can drive both transformation and fitting,
illustrating with a power and logarithm family of transformations and mixtures
of Gaussian (normal) distributions, which allow use of efficient influence
diagram methods. The fitting procedure in this case is the well-known EM
algorithm. The selection of the number of components in a fitted mixture
distribution is automated with an objective that trades off accuracy and
computational cost.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:29 GMT"
}
] | 1,362,700,800,000 | [
[
"Poland",
"William B.",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1303.1477 | Prakash P. Shenoy | Prakash P. Shenoy | Valuation Networks and Conditional Independence | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-191-199 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Valuation networks have been proposed as graphical representations of
valuation-based systems (VBSs). The VBS framework is able to capture many
uncertainty calculi including probability theory, Dempster-Shafer's
belief-function theory, Spohn's epistemic belief theory, and Zadeh's
possibility theory. In this paper, we show how valuation networks encode
conditional independence relations. For the probabilistic case, the class of
probability models encoded by valuation networks includes undirected graph
models, directed acyclic graph models, directed balloon graph models, and
recursive causal graph models.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:35 GMT"
}
] | 1,362,700,800,000 | [
[
"Shenoy",
"Prakash P.",
""
]
] |
1303.1478 | Solomon Eyal Shimony | Solomon Eyal Shimony | Relevant Explanations: Allowing Disjunctive Assignments | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-200-207 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relevance-based explanation is a scheme in which partial assignments to
Bayesian belief network variables are explanations (abductive conclusions). We
allow variables to remain unassigned in explanations as long as they are
irrelevant to the explanation, where irrelevance is defined in terms of
statistical independence. When multiple-valued variables exist in the system,
especially when subsets of values correspond to natural types of events, the
over specification problem, alleviated by independence-based explanation,
resurfaces. As a solution to that, as well as for addressing the question of
explanation specificity, it is desirable to collapse such a subset of values
into a single value on the fly. The equivalent method, which is adopted here,
is to generalize the notion of assignments to allow disjunctive assignments. We
proceed to define generalized independence based explanations as maximum
posterior probability independence based generalized assignments (GIB-MAPs).
GIB assignments are shown to have certain properties that ease the design of
algorithms for computing GIB-MAPs. One such algorithm is discussed here, as
well as suggestions for how other algorithms may be adapted to compute
GIB-MAPs. GIB-MAP explanations still suffer from instability, a problem which
may be addressed using ?approximate? conditional independence as a condition
for irrelevance.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:41 GMT"
}
] | 1,362,700,800,000 | [
[
"Shimony",
"Solomon Eyal",
""
]
] |
1303.1479 | Sampath Srinivas | Sampath Srinivas | A Generalization of the Noisy-Or Model | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-208-215 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Noisy-Or model is convenient for describing a class of uncertain
relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or
model for Boolean variables. Here we generalize the model to nary input and
output variables and to arbitrary functions other than the Boolean OR function.
This generalization is a useful modeling aid for construction of Bayesian
networks. We illustrate with some examples including digital circuit diagnosis
and network reliability analysis.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:46 GMT"
}
] | 1,362,700,800,000 | [
[
"Srinivas",
"Sampath",
""
]
] |
1303.1480 | Fahiem Bacchus | Fahiem Bacchus | Using First-Order Probability Logic for the Construction of Bayesian
Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-219-226 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a mechanism for constructing graphical models, specifically
Bayesian networks, from a knowledge base of general probabilistic information.
The unique feature of our approach is that it uses a powerful first-order
probabilistic logic for expressing the general knowledge base. This logic
allows for the representation of a wide range of logical and probabilistic
information. The model construction procedure we propose uses notions from
direct inference to identify pieces of local statistical information from the
knowledge base that are most appropriate to the particular event we want to
reason about. These pieces are composed to generate a joint probability
distribution specified as a Bayesian network. Although there are fundamental
difficulties in dealing with fully general knowledge, our procedure is
practical for quite rich knowledge bases and it supports the construction of a
far wider range of networks than allowed for by current template technology.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:51 GMT"
}
] | 1,362,700,800,000 | [
[
"Bacchus",
"Fahiem",
""
]
] |
1303.1481 | Marie desJardins | Marie desJardins | Representing and Reasoning With Probabilistic Knowledge: A Bayesian
Approach | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-227-234 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for
autonomous learning in probabilistic domains [desJardins, 1992] that
incorporates innovative techniques for using the agent's existing knowledge to
guide and constrain the learning process and for representing, reasoning with,
and learning probabilistic knowledge. This paper describes the probabilistic
representation and inference mechanism used in PAGODA. PAGODA forms theories
about the effects of its actions and the world state on the environment over
time. These theories are represented as conditional probability distributions.
A restriction is imposed on the structure of the theories that allows the
inference mechanism to find a unique predicted distribution for any action and
world state description. These restricted theories are called uniquely
predictive theories. The inference mechanism, Probability Combination using
Independence (PCI), uses minimal independence assumptions to combine the
probabilities in a theory to make probabilistic predictions.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:20:56 GMT"
}
] | 1,362,700,800,000 | [
[
"desJardins",
"Marie",
""
]
] |
1303.1482 | John W. Egar | John W. Egar, Mark A. Musen | Graph-Grammar Assistance for Automated Generation of Influence Diagrams | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-235-242 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most difficult aspects of modeling complex dilemmas in
decision-analytic terms is composing a diagram of relevance relations from a
set of domain concepts. Decision models in domains such as medicine, however,
exhibit certain prototypical patterns that can guide the modeling process.
Medical concepts can be classified according to semantic types that have
characteristic positions and typical roles in an influence-diagram model. We
have developed a graph-grammar production system that uses such inherent
interrelationships among medical terms to facilitate the modeling of medical
decisions.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:02 GMT"
}
] | 1,362,700,800,000 | [
[
"Egar",
"John W.",
""
],
[
"Musen",
"Mark A.",
""
]
] |
1303.1483 | Wai Lam | Wai Lam, Fahiem Bacchus | Using Causal Information and Local Measures to Learn Bayesian Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-243-250 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous work we developed a method of learning Bayesian Network models
from raw data. This method relies on the well known minimal description length
(MDL) principle. The MDL principle is particularly well suited to this task as
it allows us to tradeoff, in a principled way, the accuracy of the learned
network against its practical usefulness. In this paper we present some new
results that have arisen from our work. In particular, we present a new local
way of computing the description length. This allows us to make significant
improvements in our search algorithm. In addition, we modify our algorithm so
that it can take into account partial domain information that might be provided
by a domain expert. The local computation of description length also opens the
door for local refinement of an existent network. The feasibility of our
approach is demonstrated by experiments involving networks of a practical size.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:10 GMT"
}
] | 1,362,700,800,000 | [
[
"Lam",
"Wai",
""
],
[
"Bacchus",
"Fahiem",
""
]
] |
1303.1484 | Ron Musick | Ron Musick | Minimal Assumption Distribution Propagation in Belief Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-251-258 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As belief networks are used to model increasingly complex situations, the
need to automatically construct them from large databases will become
paramount. This paper concentrates on solving a part of the belief network
induction problem: that of learning the quantitative structure (the conditional
probabilities), given the qualitative structure. In particular, a theory is
presented that shows how to propagate inference distributions in a belief
network, with the only assumption being that the given qualitative structure is
correct. Most inference algorithms must make at least this assumption. The
theory is based on four network transformations that are sufficient for any
inference in a belief network. Furthermore, the claim is made that contrary to
popular belief, error will not necessarily grow as the inference chain grows.
Instead, for QBN belief nets induced from large enough samples, the error is
more likely to decrease as the size of the inference chain increases.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:15 GMT"
}
] | 1,362,700,800,000 | [
[
"Musick",
"Ron",
""
]
] |
1303.1485 | Moninder Singh | Moninder Singh, Marco Valtorta | An Algorithm for the Construction of Bayesian Network Structures from
Data | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-259-265 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previous algorithms for the construction of Bayesian belief network
structures from data have been either highly dependent on conditional
independence (CI) tests, or have required an ordering on the nodes to be
supplied by the user. We present an algorithm that integrates these two
approaches - CI tests are used to generate an ordering on the nodes from the
database which is then used to recover the underlying Bayesian network
structure using a non CI based method. Results of preliminary evaluation of the
algorithm on two networks (ALARM and LED) are presented. We also discuss some
algorithm performance issues and open problems.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:21 GMT"
}
] | 1,362,700,800,000 | [
[
"Singh",
"Moninder",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1303.1486 | Joe Suzuki | Joe Suzuki | A Construction of Bayesian Networks from Databases Based on an MDL
Principle | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-266-273 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses learning stochastic rules especially on an
inter-attribute relation based on a Minimum Description Length (MDL) principle
with a finite number of examples, assuming an application to the design of
intelligent relational database systems. The stochastic rule in this paper
consists of a model giving the structure like the dependencies of a Bayesian
Belief Network (BBN) and some stochastic parameters each indicating a
conditional probability of an attribute value given the state determined by the
other attributes' values in the same record. Especially, we propose the
extended version of the algorithm of Chow and Liu in that our learning
algorithm selects the model in the range where the dependencies among the
attributes are represented by some general plural number of trees.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:27 GMT"
}
] | 1,362,700,800,000 | [
[
"Suzuki",
"Joe",
""
]
] |
1303.1487 | Soe-Tsyr Yuan | Soe-Tsyr Yuan | Knowledge-Based Decision Model Construction for Hierarchical Diagnosis:
A Preliminary Report | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-274-281 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous methods for probabilistic reasoning in large, complex belief or
decision networks are currently being developed. There has been little research
on automating the dynamic, incremental construction of decision models. A
uniform value-driven method of decision model construction is proposed for the
hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is
formulated as a stochastic process and modeled using influence diagrams. Given
observations, this method creates decision models in order to obtain the best
actions sequentially for locating and repairing a fault at minimum cost. This
method construct decision models incrementally, interleaving probe actions with
model construction and evaluation. The method treats meta-level and baselevel
tasks uniformly. That is, the method takes a decision-theoretic look at the
control of search in causal pathways and structural hierarchies.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:32 GMT"
}
] | 1,362,700,800,000 | [
[
"Yuan",
"Soe-Tsyr",
""
]
] |
1303.1488 | Lisa J. Burnell | Lisa J. Burnell, Eric J. Horvitz | A Synthesis of Logical and Probabilistic Reasoning for Program
Understanding and Debugging | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-285-291 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe the integration of logical and uncertain reasoning methods to
identify the likely source and location of software problems. To date, software
engineers have had few tools for identifying the sources of error in complex
software packages. We describe a method for diagnosing software problems
through combining logical and uncertain reasoning analyses. Our preliminary
results suggest that such methods can be of value in directing the attention of
software engineers to paths of an algorithm that have the highest likelihood of
harboring a programming error.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:38 GMT"
}
] | 1,362,700,800,000 | [
[
"Burnell",
"Lisa J.",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1303.1489 | Peter Che | Peter Che, Richard E. Neapolitan, James Kenevan, Martha Evens | An Implementation of a Method for Computing the Uncertainty in Inferred
Probabilities in Belief Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-292-300 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years the belief network has been used increasingly to model
systems in Al that must perform uncertain inference. The development of
efficient algorithms for probabilistic inference in belief networks has been a
focus of much research in AI. Efficient algorithms for certain classes of
belief networks have been developed, but the problem of reporting the
uncertainty in inferred probabilities has received little attention. A system
should not only be capable of reporting the values of inferred probabilities
and/or the favorable choices of a decision; it should report the range of
possible error in the inferred probabilities and/or choices. Two methods have
been developed and implemented for determining the variance in inferred
probabilities in belief networks. These methods, the Approximate Propagation
Method and the Monte Carlo Integration Method are discussed and compared in
this paper.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:44 GMT"
}
] | 1,362,700,800,000 | [
[
"Che",
"Peter",
""
],
[
"Neapolitan",
"Richard E.",
""
],
[
"Kenevan",
"James",
""
],
[
"Evens",
"Martha",
""
]
] |
1303.1490 | Bruce D'Ambrosio | Bruce D'Ambrosio | Incremental Probabilistic Inference | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-301-308 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Propositional representation services such as truth maintenance systems offer
powerful support for incremental, interleaved, problem-model construction and
evaluation. Probabilistic inference systems, in contrast, have lagged behind in
supporting this incrementality typically demanded by problem solvers. The
problem, we argue, is that the basic task of probabilistic inference is
typically formulated at too large a grain-size. We show how a system built
around a smaller grain-size inference task can have the desired incrementality
and serve as the basis for a low-level (propositional) probabilistic
representation service.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:48 GMT"
}
] | 1,362,700,800,000 | [
[
"D'Ambrosio",
"Bruce",
""
]
] |
1303.1491 | Thomas L. Dean | Thomas L. Dean, Leslie Pack Kaelbling, Jak Kirman, Ann Nicholson | Deliberation Scheduling for Time-Critical Sequential Decision Making | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-309-316 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a method for time-critical decision making involving sequential
tasks and stochastic processes. The method employs several iterative refinement
routines for solving different aspects of the decision making problem. This
paper concentrates on the meta-level control problem of deliberation
scheduling, allocating computational resources to these routines. We provide
different models corresponding to optimization problems that capture the
different circumstances and computational strategies for decision making under
time constraints. We consider precursor models in which all decision making is
performed prior to execution and recurrent models in which decision making is
performed in parallel with execution, accounting for the states observed during
execution and anticipating future states. We describe algorithms for precursor
and recurrent models and provide the results of our empirical investigations to
date.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:21:54 GMT"
}
] | 1,362,700,800,000 | [
[
"Dean",
"Thomas L.",
""
],
[
"Kaelbling",
"Leslie Pack",
""
],
[
"Kirman",
"Jak",
""
],
[
"Nicholson",
"Ann",
""
]
] |
1303.1492 | Marek J. Druzdzel | Marek J. Druzdzel, Max Henrion | Intercausal Reasoning with Uninstantiated Ancestor Nodes | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-317-325 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intercausal reasoning is a common inference pattern involving probabilistic
dependence of causes of an observed common effect. The sign of this dependence
is captured by a qualitative property called product synergy. The current
definition of product synergy is insufficient for intercausal reasoning where
there are additional uninstantiated causes of the common effect. We propose a
new definition of product synergy and prove its adequacy for intercausal
reasoning with direct and indirect evidence for the common effect. The new
definition is based on a new property matrix half positive semi-definiteness, a
weakened form of matrix positive semi-definiteness.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:00 GMT"
}
] | 1,362,700,800,000 | [
[
"Druzdzel",
"Marek J.",
""
],
[
"Henrion",
"Max",
""
]
] |
1303.1493 | Dan Geiger | Dan Geiger, David Heckerman | Inference Algorithms for Similarity Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-326-334 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine two types of similarity networks each based on a distinct notion
of relevance. For both types of similarity networks we present an efficient
inference algorithm that works under the assumption that every event has a
nonzero probability of occurrence. Another inference algorithm is developed for
type 1 similarity networks that works under no restriction, albeit less
efficiently.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:08 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:49:37 GMT"
}
] | 1,431,993,600,000 | [
[
"Geiger",
"Dan",
""
],
[
"Heckerman",
"David",
""
]
] |
1303.1494 | Paul E. Lehner | Paul E. Lehner, Azar Sadigh | Two Procedures for Compiling Influence Diagrams | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-335-341 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two algorithms are presented for "compiling" influence diagrams into a set of
simple decision rules. These decision rules define simple-to-execute, complete,
consistent, and near-optimal decision procedures. These compilation algorithms
can be used to derive decision procedures for human teams solving time
constrained decision problems.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:13 GMT"
}
] | 1,362,700,800,000 | [
[
"Lehner",
"Paul E.",
""
],
[
"Sadigh",
"Azar",
""
]
] |
1303.1495 | Zhaoyu Li | Zhaoyu Li, Bruce D'Ambrosio | An efficient approach for finding the MPE in belief networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-342-349 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a belief network with evidence, the task of finding the I most probable
explanations (MPE) in the belief network is that of identifying and ordering
the I most probable instantiations of the non-evidence nodes of the belief
network. Although many approaches have been proposed for solving this problem,
most work only for restricted topologies (i.e., singly connected belief
networks). In this paper, we will present a new approach for finding I MPEs in
an arbitrary belief network. First, we will present an algorithm for finding
the MPE in a belief network. Then, we will present a linear time algorithm for
finding the next MPE after finding the first MPE. And finally, we will discuss
the problem of finding the MPE for a subset of variables of a belief network,
and show that the problem can be efficiently solved by this approach.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:18 GMT"
}
] | 1,362,700,800,000 | [
[
"Li",
"Zhaoyu",
""
],
[
"D'Ambrosio",
"Bruce",
""
]
] |
1303.1496 | Todd Michael Mansell | Todd Michael Mansell | A Method for Planning Given Uncertain and Incomplete Information | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-350-358 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes ongoing research into planning in an uncertain
environment. In particular, it introduces U-Plan, a planning system that
constructs quantitatively ranked plans given an incomplete description of the
state of the world. U-Plan uses a DempsterShafer interval to characterise
uncertain and incomplete information about the state of the world. The planner
takes as input what is known about the world, and constructs a number of
possible initial states with representations at different abstraction levels. A
plan is constructed for the initial state with the greatest support, and this
plan is tested to see if it will work for other possible initial states. All,
part, or none of the existing plans may be used in the generation of the plans
for the remaining possible worlds. Planning takes place in an abstraction
hierarchy where strategic decisions are made before tactical decisions. A
super-plan is then constructed, based on merging the set of plans and the
appropriately timed acquisition of essential knowledge, which is used to decide
between plan alternatives. U-Plan usually produces a super-plan in less time
than a classical planner would take to produce a set of plans, one for each
possible world.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:24 GMT"
}
] | 1,362,700,800,000 | [
[
"Mansell",
"Todd Michael",
""
]
] |
1303.1497 | David L Poole | David L. Poole | The use of conflicts in searching Bayesian networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-359-367 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses how conflicts (as used by the consistency-based
diagnosis community) can be adapted to be used in a search-based algorithm for
computing prior and posterior probabilities in discrete Bayesian Networks. This
is an "anytime" algorithm, that at any stage can estimate the probabilities and
give an error bound. Whereas the most popular Bayesian net algorithms exploit
the structure of the network for efficiency, we exploit probability
distributions for efficiency; this algorithm is most suited to the case with
extreme probabilities. This paper presents a solution to the inefficiencies
found in naive algorithms, and shows how the tools of the consistency-based
diagnosis community (namely conflicts) can be used effectively to improve the
efficiency. Empirical results with networks having tens of thousands of nodes
are presented.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:29 GMT"
}
] | 1,362,700,800,000 | [
[
"Poole",
"David L.",
""
]
] |
1303.1498 | Carlos Rojas-Guzman | Carlos Rojas-Guzman, Mark A. Kramer | GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain
Systems Modeled with Bayesian Belief Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-368-375 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian belief networks can be used to represent and to reason about complex
systems with uncertain, incomplete and conflicting information. Belief networks
are graphs encoding and quantifying probabilistic dependence and conditional
independence among variables. One type of reasoning of interest in diagnosis is
called abductive inference (determination of the global most probable system
description given the values of any partial subset of variables). In some
cases, abductive inference can be performed with exact algorithms using
distributed network computations but it is an NP-hard problem and complexity
increases drastically with the presence of undirected cycles, number of
discrete states per variable, and number of variables in the network. This
paper describes an approximate method based on genetic algorithms to perform
abductive inference in large, multiply connected networks for which complexity
is a concern when using most exact methods and for which systematic search
methods are not feasible. The theoretical adequacy of the method is discussed
and preliminary experimental results are presented.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:36 GMT"
}
] | 1,362,700,800,000 | [
[
"Rojas-Guzman",
"Carlos",
""
],
[
"Kramer",
"Mark A.",
""
]
] |
1303.1499 | Sumit Sarkar | Sumit Sarkar | Using Tree-Decomposable Structures to Approximate Belief Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-376-382 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tree structures have been shown to provide an efficient framework for
propagating beliefs [Pearl,1986]. This paper studies the problem of finding an
optimal approximating tree. The star decomposition scheme for sets of three
binary variables [Lazarsfeld,1966; Pearl,1986] is shown to enhance the class of
probability distributions that can support tree structures; such structures are
called tree-decomposable structures. The logarithm scoring rule is found to be
an appropriate optimality criterion to evaluate different tree-decomposable
structures. Characteristics of such structures closest to the actual belief
network are identified using the logarithm rule, and greedy and exact
techniques are developed to find the optimal approximation.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:41 GMT"
}
] | 1,362,700,800,000 | [
[
"Sarkar",
"Sumit",
""
]
] |
1303.1500 | Ross D. Shachter | Ross D. Shachter, Pierre Ndilikilikesha | Using Potential Influence Diagrams for Probabilistic Inference and
Decision Making | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-383-390 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The potential influence diagram is a generalization of the standard
"conditional" influence diagram, a directed network representation for
probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows
efficient inference calculations corresponding exactly to those on undirected
graphs. In this paper, we explore the relationship between potential and
conditional influence diagrams and provide insight into the properties of the
potential influence diagram. In particular, we show how to convert a potential
influence diagram into a conditional influence diagram, and how to view the
potential influence diagram operations in terms of the conditional influence
diagram.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:47 GMT"
}
] | 1,362,700,800,000 | [
[
"Shachter",
"Ross D.",
""
],
[
"Ndilikilikesha",
"Pierre",
""
]
] |
1303.1501 | Tom S. Verma | Tom S. Verma, Judea Pearl | Deciding Morality of Graphs is NP-complete | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-391-399 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to find a causal explanation for data presented in the form of
covariance and concentration matrices it is necessary to decide if the graph
formed by such associations is a projection of a directed acyclic graph (dag).
We show that the general problem of deciding whether such a dag exists is
NP-complete.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:53 GMT"
}
] | 1,362,700,800,000 | [
[
"Verma",
"Tom S.",
""
],
[
"Pearl",
"Judea",
""
]
] |
1303.1502 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, Runping Qi, David L. Poole | Incremental computation of the value of perfect information in
stepwise-decomposable influence diagrams | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-400-407 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To determine the value of perfect information in an influence diagram, one
needs first to modify the diagram to reflect the change in information
availability, and then to compute the optimal expected values of both the
original diagram and the modified diagram. The value of perfect information is
the difference between the two optimal expected values. This paper is about how
to speed up the computation of the optimal expected value of the modified
diagram by making use of the intermediate computation results obtained when
computing the optimal expected value of the original diagram.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:22:59 GMT"
}
] | 1,362,700,800,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Qi",
"Runping",
""
],
[
"Poole",
"David L.",
""
]
] |
1303.1503 | Salem Benferhat | Salem Benferhat, Didier Dubois, Henri Prade | Argumentative inference in uncertain and inconsistent knowledge bases | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-411-419 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents and discusses several methods for reasoning from
inconsistent knowledge bases. A so-called argumentative-consequence relation
taking into account the existence of consistent arguments in favor of a
conclusion and the absence of consistent arguments in favor of its contrary, is
particularly investigated. Flat knowledge bases, i.e. without any priority
between their elements, as well as prioritized ones where some elements are
considered as more strongly entrenched than others are studied under different
consequence relations. Lastly a paraconsistent-like treatment of prioritized
knowledge bases is proposed, where both the level of entrenchment and the level
of paraconsistency attached to a formula are propagated. The priority levels
are handled in the framework of possibility theory.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:05 GMT"
}
] | 1,362,700,800,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1303.1504 | Adnan Darwiche | Adnan Darwiche | Argument Calculus and Networks | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-420-427 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major reason behind the success of probability calculus is that it
possesses a number of valuable tools, which are based on the notion of
probabilistic independence. In this paper, I identify a notion of logical
independence that makes some of these tools available to a class of
propositional databases, called argument databases. Specifically, I suggest a
graphical representation of argument databases, called argument networks, which
resemble Bayesian networks. I also suggest an algorithm for reasoning with
argument networks, which resembles a basic algorithm for reasoning with
Bayesian networks. Finally, I show that argument networks have several
applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:11 GMT"
}
] | 1,362,700,800,000 | [
[
"Darwiche",
"Adnan",
""
]
] |
1303.1505 | John Fox | John Fox, Paul J. Krause, Morten Elvang-G{\o}ransson | Argumentation as a General Framework for Uncertain Reasoning | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-428-434 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation is the process of constructing arguments about propositions,
and the assignment of statements of confidence to those propositions based on
the nature and relative strength of their supporting arguments. The process is
modelled as a labelled deductive system, in which propositions are doubly
labelled with the grounds on which they are based and a representation of the
confidence attached to the argument. Argument construction is captured by a
generalized argument consequence relation based on the ^,--fragment of minimal
logic. Arguments can be aggregated by a variety of numeric and symbolic
flattening functions. This approach appears to shed light on the common logical
structure of a variety of quantitative, qualitative and defeasible uncertainty
calculi.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:15 GMT"
}
] | 1,362,700,800,000 | [
[
"Fox",
"John",
""
],
[
"Krause",
"Paul J.",
""
],
[
"Elvang-Gøransson",
"Morten",
""
]
] |
1303.1506 | Simon Parsons | Simon Parsons, E. H. Mamdani | On reasoning in networks with qualitative uncertainty | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-435-442 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper some initial work towards a new approach to qualitative
reasoning under uncertainty is presented. This method is not only applicable to
qualitative probabilistic reasoning, as is the case with other methods, but
also allows the qualitative propagation within networks of values based upon
possibility theory and Dempster-Shafer evidence theory. The method is applied
to two simple networks from which a large class of directed graphs may be
constructed. The results of this analysis are used to compare the qualitative
behaviour of the three major quantitative uncertainty handling formalisms, and
to demonstrate that the qualitative integration of the formalisms is possible
under certain assumptions.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:21 GMT"
}
] | 1,362,700,800,000 | [
[
"Parsons",
"Simon",
""
],
[
"Mamdani",
"E. H.",
""
]
] |
1303.1507 | Michael S. K. M. Wong | Michael S. K. M. Wong, Z. W. Wang | Qualitative Measures of Ambiguity | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-443-450 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a qualitative measure of ambiguity and analyses its
relationship with other measures of uncertainty. Probability measures relative
likelihoods, while ambiguity measures vagueness surrounding those judgments.
Ambiguity is an important representation of uncertain knowledge. It deals with
a different, type of uncertainty modeled by subjective probability or belief.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:27 GMT"
}
] | 1,362,700,800,000 | [
[
"Wong",
"Michael S. K. M.",
""
],
[
"Wang",
"Z. W.",
""
]
] |
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